This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.
Machine Learning in Finance vs Machine Learning in Tech, Part II
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From the course by New York University Tandon School of Engineering
Guided Tour of Machine Learning in Finance
148 ratings
New York University Tandon School of Engineering
148 ratings
Course 1 of 4 in the Specialization Machine Learning and Reinforcement Learning in Finance
From the lesson
Artificial Intelligence & Machine Learning
Meet the Instructors
Igor Halperin
Now, let's compare machine learning in Finance with machine learning in Tech
in terms of tasks.
First, let's start with applications of machine learning in Tech.
Most common applications of machine learning in Tech are image recognition and
various natural language processing tasks such as text classification,
sentiment analysis, machine translation and so on.
According to our specification of machine learning and to perception tasks, and
action tasks, this applications belong in the class of perception tasks.
This is because for these tasks there is only a fixed single action
that the algorithm has to perform well.
For example to translate the text from one language to another,
this problem are usually solved using the supervise learning,
sometimes in the combination with unsupervised learning.
This, for example so fetch in tasks in Tech.
We can mention various problems in computational advertising, robotics,
and self driving cars.
For this task, the reinforcement learning provides the most natural framework and
is used for methods in practice,
along with methods based on supervised and unsupervised learning.
Now let's see what kind of perception and action tasks are encountered in Finance.
First, there are many types of forecasting problems in Finance that belong
in the class of perception tasks.
They include, in particular, predictions of prices of securities, such as stocks,
bonds, or commodities.
For this problem, supervised and
unsupervised learning are the methods of choice.
Other forecasting tasks in Finance include predicting actions of individual players.
For example, we may want to predict actions of corporate,
such as dividend payments, defaults, mergers and so on.
Another problem is prediction of actions of individuals with ample defaults and
loans, fraud, or anti-money-laundering.
It turns out that for such prediction tasks, we can use not only supervised and
unsupervised learning, but also reinforcement learning.
Other typical perception tasks which are commonly used in Finance
deal with valuation.
For example, valuation of assets such as stocks, futures, commodities,
and bonds is related to prediction problems, but is different from it.
Another classic valuation problem in Finance is derivative pricing.
Even though the most conventional way for
derivatives pricing is via using parameterized stochastic models,
the problem lends itself quite naturally for machine learning.
In the last course of this specialization,
we will see how reinforcement learning can be used to price financial derivatives.
Now let's note something interesting here.
Remember that in Tech, perception tasks are solved using supervised and
unsupervised learning, while reinforcement learning is only used for action tasks.
Yet, in Finance, it sounds as even perception tasks can employ
methods based on reinforcement learning.
We can ask, what is the source of such difference between perception tasks
in Finance and Tech?
And the short answer I can offer would be this.
In Finance, perception tasks are often about the future.
For example, about prediction of dividends paid by company in the next quarter.
However, this future is driven, to a large extent,
by specific decisions of decision makers.
For example, next quarter dividends are not some sort of random stochastic
process, but rather decided by a company management.
And this is exactly the reason reinforcement learning might be a good
conceptual framework in this subject.
This is because reinforcement learning directly models the process of
decision-making.
Therefore, in the example of corporate dividends, it tries to explain the physics
of the process rather blindly, if you like, from a purely statistical approach.
Now, let's consider action tasks in Finance.
Typical example of this in Finance would be in trading and asset management.
Including the problems of optimal execution for brokerage trading,
optimal strategies for day trading, or active portfolio management.
Because they hold you with the problem of choosing optimal action,
reinforcement learning is a very natural framework for these problems.
Further examples of action task in Finance involves various
decision making processes in banking.
For example loan approvals, credit and operational risk management or
decision making in compliance analytics, or
anti-money laundering would all be examples of action tasks.
Again, reinforcement learning is a very naturall framework for such settings.
So let's summarize what we found so far.
We found perception tasks in Finance might sometimes be very different from
perception tasks in Tech.
Such difference arises for forecasting tasks that involve predicting
future actions, so in rational or semi-rational market agents.
For example, actions of corporations or individuals.
Reinforcement network is a framework that fits very naturally in such problems.
Moreover the fact that the reinforcement learning appears as a method of choice for
both perception and action tasks in Finance unlike Tech.
When it only appears in action tasks, point to important qualitative
differences between machine learning in Finance and machine learning in Tech.
In addition,
there are also some important quantitative differences that we will discuss next.
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